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Source channel @githubtrending · Post #15006 · Jul 29

#go#cli#event_driven#event_driven_architecture#queues#serverless#serverless_functions#workflow_engine#workflows Inngest lets you write reliable, long-running background functions called durable workflows that automatically handle retries, scheduling, and state management without needing to manage infrastructure like queues or servers. You write functions in your preferred language using their SDKs, run and test them locally with the Inngest Dev Server, then deploy them on your own infrastructure or Inngest’s platform. It supports complex workflows with steps that retry on failure, concurrency control, and event triggers. This saves you time and effort by simplifying event-driven app development, improving reliability, and scaling automatically without extra setup. It also offers tools for monitoring and managing workflows easily. https://github.com/inngest/inngest

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@githubtrending · Post #14659 · 05/01/2025, 03:30 PM

#cplusplus#arm#convolution#deep_learning#embedded_devices#llm#machine_learning#ml#mnn#transformer#vulkan#winograd_algorithm MNN is a lightweight and efficient deep learning framework that helps run AI models on mobile devices and other small devices. It supports many types of AI models and can handle tasks like image recognition and language processing quickly and locally on your device. This means you can use AI features without needing to send data to the cloud, which improves privacy and speed. MNN is used in many apps, including those from Alibaba, and supports various platforms like Android and iOS. It also helps reduce the size of AI models, making them faster and more efficient. https://github.com/alibaba/MNN